






"""simple docstring"""







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_						:     Optional[int]							  =   logging.get_logger(__name__)

lowerCAmelCase_						:     str							  =   '''▁'''

lowerCAmelCase_						:     Dict							  =   {'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCAmelCase_						:     Any							  =   {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

lowerCAmelCase_						:     int							  =   {
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}

# fmt: off
lowerCAmelCase_						:     Dict							  =   ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']





class 	UpperCamelCase_			(							a_	):
  _A							:       Union[str, Any]					=						VOCAB_FILES_NAMES
  _A							:       Any					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _A							:       Dict					=						PRETRAINED_VOCAB_FILES_MAP
  _A							:       List[str]					=						['input_ids', 'attention_mask']

  _A							:       Union[str, Any]					=						[]
  _A							:       str					=						[]



  def __init__(   self			,		snake_case__			,		snake_case__=None			,		snake_case__=None			,		snake_case__="</s>"			,		snake_case__="</s>"			,		snake_case__="<s>"			,		snake_case__="<unk>"			,		snake_case__="<pad>"			,		snake_case__="<mask>"			,		snake_case__ = None			,		**snake_case__			,		)      ->      None:



         """simple docstring"""





         UpperCAmelCase						     =      AddedToken(snake_case__			,		lstrip=snake_case__			,		rstrip=snake_case__					) if isinstance(snake_case__			,		snake_case__					) else mask_token

         UpperCAmelCase						     =      {} if sp_model_kwargs is None else sp_model_kwargs

         UpperCAmelCase						     =      kwargs.get("""additional_special_tokens"""			,		[]					)
         kwargs["additional_special_tokens"] += [
             code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             src_lang=snake_case__			,		tgt_lang=snake_case__			,		eos_token=snake_case__			,		unk_token=snake_case__			,		sep_token=snake_case__			,		cls_token=snake_case__			,		pad_token=snake_case__			,		mask_token=snake_case__			,		sp_model_kwargs=self.sp_model_kwargs			,		**snake_case__			,		)

         UpperCAmelCase						     =      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(str(snake_case__					)					)
         UpperCAmelCase						     =      vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # Mimic fairseq token-to-id alignment for the first 4 token
         UpperCAmelCase						     =      {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         UpperCAmelCase						     =      1

         UpperCAmelCase						     =      len(self.sp_model					)
         UpperCAmelCase						     =      {
             code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(snake_case__					)
         }
         UpperCAmelCase						     =      {v: k for k, v in self.lang_code_to_id.items()}
         UpperCAmelCase						     =      len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset

         self.fairseq_tokens_to_ids.update(self.lang_code_to_id					)
         UpperCAmelCase						     =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

         UpperCAmelCase						     =      src_lang if src_lang is not None else """en_XX"""
         UpperCAmelCase						     =      self.lang_code_to_id[self._src_lang]
         UpperCAmelCase						     =      tgt_lang
         self.set_src_lang_special_tokens(self._src_lang					)



  @property
  def 		UpperCamelCase_					(   self					)      ->      int:



         """simple docstring"""





         return len(self.sp_model					) + len(self.lang_code_to_id					) + self.fairseq_offset + 1  # Plus 1 for the mask token



  @property
  def 		UpperCamelCase_					(   self					)      ->      str:



         """simple docstring"""





         return self._src_lang



  @src_lang.setter
  def 		UpperCamelCase_					(   self			,		snake_case__					)      ->      None:



         """simple docstring"""





         UpperCAmelCase						     =      new_src_lang
         self.set_src_lang_special_tokens(self._src_lang					)



  def __getstate__(   self					)      ->      Dict:



         """simple docstring"""





         UpperCAmelCase						     =      self.__dict__.copy()
         UpperCAmelCase						     =      None
         return state



  def __setstate__(   self			,		snake_case__					)      ->      None:



         """simple docstring"""





         UpperCAmelCase						     =      d

         # for backward compatibility
         if not hasattr(self			,		"""sp_model_kwargs"""					):
                UpperCAmelCase						     =      {}

         UpperCAmelCase						     =      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(self.vocab_file					)



  def 		UpperCamelCase_					(   self					)      ->      Dict:



         """simple docstring"""





         UpperCAmelCase						     =      {self.convert_ids_to_tokens(snake_case__					): i for i in range(self.vocab_size					)}
         vocab.update(self.added_tokens_encoder					)
         return vocab



  def 		UpperCamelCase_					(   self			,		snake_case__					)      ->      List[str]:



         """simple docstring"""





         return self.sp_model.encode(snake_case__			,		out_type=snake_case__					)



  def 		UpperCamelCase_					(   self			,		snake_case__					)      ->      int:



         """simple docstring"""





         if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
         UpperCAmelCase						     =      self.sp_model.PieceToId(snake_case__					)

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



  def 		UpperCamelCase_					(   self			,		snake_case__					)      ->      str:



         """simple docstring"""





         if index in self.fairseq_ids_to_tokens:
                return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset					)



  def 		UpperCamelCase_					(   self			,		snake_case__					)      ->      Optional[Any]:



         """simple docstring"""





         UpperCAmelCase						     =      []
         UpperCAmelCase						     =      """"""
         UpperCAmelCase						     =      False
         for token in tokens:
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                       if not prev_is_special:
                              out_string += " "
                       out_string += self.sp_model.decode(snake_case__					) + token
                       UpperCAmelCase						     =      True
                       UpperCAmelCase						     =      []
                else:
                       current_sub_tokens.append(snake_case__					)
                       UpperCAmelCase						     =      False
         out_string += self.sp_model.decode(snake_case__					)
         return out_string.strip()



  def 		UpperCamelCase_					(   self			,		snake_case__			,		snake_case__ = None					)      ->      Tuple[str]:



         """simple docstring"""





         if not os.path.isdir(snake_case__					):
                logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
                return
         UpperCAmelCase						     =      os.path.join(
             snake_case__			,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

         if os.path.abspath(self.vocab_file					) != os.path.abspath(snake_case__					) and os.path.isfile(self.vocab_file					):
                copyfile(self.vocab_file			,		snake_case__					)
         elif not os.path.isfile(self.vocab_file					):
                with open(snake_case__			,		"""wb"""					) as fi:
                       UpperCAmelCase						     =      self.sp_model.serialized_model_proto()
                       fi.write(snake_case__					)

         return (out_vocab_file,)



  def 		UpperCamelCase_					(   self			,		snake_case__			,		snake_case__ = None			,		snake_case__ = False					)      ->      List[int]:



         """simple docstring"""





         if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=snake_case__			,		token_ids_a=snake_case__			,		already_has_special_tokens=snake_case__					)

         UpperCAmelCase						     =      [1] * len(self.prefix_tokens					)
         UpperCAmelCase						     =      [1] * len(self.suffix_tokens					)
         if token_ids_a is None:
                return prefix_ones + ([0] * len(snake_case__					)) + suffix_ones
         return prefix_ones + ([0] * len(snake_case__					)) + ([0] * len(snake_case__					)) + suffix_ones



  def 		UpperCamelCase_					(   self			,		snake_case__			,		snake_case__ = None					)      ->      List[int]:



         """simple docstring"""





         if token_ids_a is None:
                return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



  def 		UpperCamelCase_					(   self			,		snake_case__			,		snake_case__			,		snake_case__			,		snake_case__			,		**snake_case__					)      ->      Union[str, Any]:



         """simple docstring"""





         if src_lang is None or tgt_lang is None:
                raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""					)
         UpperCAmelCase						     =      src_lang
         UpperCAmelCase						     =      self(snake_case__			,		add_special_tokens=snake_case__			,		return_tensors=snake_case__			,		**snake_case__					)
         UpperCAmelCase						     =      self.convert_tokens_to_ids(snake_case__					)
         UpperCAmelCase						     =      tgt_lang_id
         return inputs



  def 		UpperCamelCase_					(   self			,		snake_case__			,		snake_case__ = "en_XX"			,		snake_case__ = None			,		snake_case__ = "ro_RO"			,		**snake_case__			,		)      ->      BatchEncoding:



         """simple docstring"""





         UpperCAmelCase						     =      src_lang
         UpperCAmelCase						     =      tgt_lang
         return super().prepare_seqaseq_batch(snake_case__			,		snake_case__			,		**snake_case__					)



  def 		UpperCamelCase_					(   self					)      ->      Union[str, Any]:



         """simple docstring"""





         return self.set_src_lang_special_tokens(self.src_lang					)



  def 		UpperCamelCase_					(   self					)      ->      Optional[int]:



         """simple docstring"""





         return self.set_tgt_lang_special_tokens(self.tgt_lang					)



  def 		UpperCamelCase_					(   self			,		snake_case__					)      ->      None:



         """simple docstring"""





         UpperCAmelCase						     =      self.lang_code_to_id[src_lang]
         UpperCAmelCase						     =      [self.cur_lang_code_id]
         UpperCAmelCase						     =      [self.eos_token_id]



  def 		UpperCamelCase_					(   self			,		snake_case__					)      ->      None:



         """simple docstring"""





         UpperCAmelCase						     =      self.lang_code_to_id[tgt_lang]
         UpperCAmelCase						     =      [self.cur_lang_code_id]
         UpperCAmelCase						     =      [self.eos_token_id]






'''simple docstring'''


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, TensorType

UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """imagegpt"""
   _a    						=   ["""past_key_values"""]
   _a    						=   {
       """hidden_size""": """n_embd""",
       """max_position_embeddings""": """n_positions""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }







   def __init__(						self   ,				UpperCamelCase=512 + 1   ,				UpperCamelCase=32 * 32   ,				UpperCamelCase=512   ,				UpperCamelCase=24   ,				UpperCamelCase=8   ,				UpperCamelCase=None   ,				UpperCamelCase="quick_gelu"   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=1e-5   ,				UpperCamelCase=0.02   ,				UpperCamelCase=True   ,				UpperCamelCase=True   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				**UpperCamelCase   ,				)					->     int:
     __a								=       vocab_size
     __a								=       n_positions
     __a								=       n_embd
     __a								=       n_layer
     __a								=       n_head
     __a								=       n_inner
     __a								=       activation_function
     __a								=       resid_pdrop
     __a								=       embd_pdrop
     __a								=       attn_pdrop
     __a								=       layer_norm_epsilon
     __a								=       initializer_range
     __a								=       scale_attn_weights
     __a								=       use_cache
     __a								=       scale_attn_by_inverse_layer_idx
     __a								=       reorder_and_upcast_attn
     __a								=       tie_word_embeddings

     super().__init__(tie_word_embeddings=UpperCamelCase   ,				**UpperCamelCase   )





class  __lowercase    (	__magic_name__			):







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('input_ids', {0: 'batch', 1: 'sequence'}),
         ]   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = 1   ,				UpperCamelCase = -1   ,				UpperCamelCase = False   ,				UpperCamelCase = None   ,				UpperCamelCase = 3   ,				UpperCamelCase = 32   ,				UpperCamelCase = 32   ,				)					->     Mapping[str, Any]:
     __a								=       self._generate_dummy_images(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
     __a								=       dict(preprocessor(images=UpperCamelCase   ,				return_tensors=UpperCamelCase   )   )

     return inputs



